What is geographic data?

• Thus far: we’ve been working with data (largely) in tabular data frames, where each column represents data “attributes”
• Geographic data: includes information on location - specifically, where the observation is located on the Earth’s surface

Maps!

Longitude and latitude: the basics

Geographic coordinates

• Longitude (X) and latitude (Y) coordinates can be expressed as:
• Degrees minutes seconds (e.g. \(97^{\circ}21'37''W\), \(32^{\circ}42'38''N\))
• Decimal degrees (e.g. \(-97.36\), \(32.71\))

Conversion between DMS and decimal degrees:

\[DD = D + \frac{M}{60} + \frac{S}{3600}\]

Coordinate systems

• Geographic coordinate system: coordinate system based on latitude and longitude (coordinates on a sphere)
• Projected coordinate system: coordinate system projected onto a two-dimensional surface (coordinates on a plane)
• Example: http://bl.ocks.org/mbostock/raw/5731632

Map projections

Tiled mapping and Web Mercator

Map projections and distortion

Examples:

• http://bl.ocks.org/curran/raw/01aa2685f083b6c1b9fb
• http://bl.ocks.org/enjalot/raw/bd552e711b8325c64729

Vector data

Raster data

Vector data: CSV files

• Example: Starbucks locations in Chicago
• Data download: http://personal.tcu.edu/kylewalker/data/chicago_starbucks.csv

Let’s make a map!

• CARTO: https://carto.com/signup

Mapping point data

Vector data: the shapefile

• Shapefile: common format for encoding vector geographic data
• Three required files: .shp, .dbf, and .shx; .prj recommended

Vector data: GeoJSON

• Mexico’s states as GeoJSON

Mapping polygons

Should you map?

• Beware the “amazing map”…

Maps vs. charts

• For discussion: which visualization is more effective for showing differences in our data?

Chart for comparison

Code to reproduce the chart

import pandas as pd, seaborn as sns, matplotlib.pyplot as plt
sns.set(style="whitegrid")
df = pd.read_csv("http://personal.tcu.edu/kylewalker/mexico.csv")

plt.figure(figsize = (10, 8))

p = sns.stripplot(data = df.sort_values('pri10', ascending = False), 
                  x = 'pri10', y = 'name', palette = "RdPu_d", 
                  orient = 'h', size = 8)
p.set(xlabel = "% of workforce in primary sector", 
      xlim = (0, 50), ylabel = "")
p.axes.xaxis.grid(False)
p.axes.yaxis.grid(True)
sns.despine(left = True, bottom = True)